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Computer Science > Learning

Title:
Spectral Learning of Binomial HMMs for DNA Methylation Data

Abstract: We consider learning parameters of Binomial Hidden Markov Models, which may
be used to model DNA methylation data. The standard algorithm for the problem
is EM, which is computationally expensive for sequences of the scale of the
mammalian genome. Recently developed spectral algorithms can learn parameters
of latent variable models via tensor decomposition, and are highly efficient
for large data. However, these methods have only been applied to categorial
HMMs, and the main challenge is how to extend them to Binomial HMMs while still
retaining computational efficiency. We address this challenge by introducing a
new feature-map based approach that exploits specific properties of Binomial
HMMs. We provide theoretical performance guarantees for our algorithm and
evaluate it on real DNA methylation data.